Renewable power systems are becoming more affordable and provide better options than fossil-fuel generation, for not only the environment, but a benefit of a reduced cost of operation. Methods to optimize charging batteries from renewable technologies is an important subject for off-grid and micro-grids, and is becoming more relevant for larger installations. Overcharging or undercharging the battery can result in failure and reduction of battery life. The Arduino hybrid MPPT controller takes the advantage of solar and wind energy sources by controlling two systems simultaneously. The ability to manage two systems with one controller is better for an overall production of energy, cost, and manageability, at a minor expense of efficiency. The hybrid MPPT uses two synchronous buck DC-DC converters to control both wind and solar. The hybrid MPPT performed at a maximum of 93.6% efficiency, while the individual controller operated at a maximum 97.1% efficiency when working on the bench test. When designing the controller to manage power production from a larger generator, the inductor size was too large due to the frequency provided by the Arduino. A larger inductor means less allowable current to flow before the inductor becomes over saturated, reducing the efficiency of the controller. Utilizing …

One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.

This Master thesis proposes a human-computer interface for individual with limited hand movements that incorporate the use of facial gesture as a means of communication. The system recognizes faces and extracts facial gestures to map them into Morse code that would be translated in English in real time. The system is implemented on a MACBOOK computer using Python software, OpenCV library, and Dlib library. The system is tested by 6 students. Five of the testers were not familiar with Morse code. They performed the experiments in an average of 90 seconds. One of the tester was familiar with Morse code and performed the experiment in 53 seconds. It is concluded that errors occurred due to variations in features of the testers, lighting conditions, and unfamiliarity with the system. Implementing an auto correction and auto prediction system will decrease typing time considerably and make the system more robust.

This thesis describes development of low cost indoor air quality (IAQ) monitoring system for research. It describes data collection of various parameters concentration present in indoor air and sends data back to host PC for further processing. Thesis gives detailed information about hardware and software implementation of IAQ monitoring system. Also discussed are building wireless ZigBee network, creating user friendly graphical user interface (GUI) and analysis of obtained results in comparison with professional benchmark system to check system reliability. Throughputs obtained are efficient enough to use system as a reliable IAQ monitor.

This thesis investigates the design and implementation of turbo codes over the GNU radio. The turbo codes is a class of iterative channel codes which demonstrates strong capability for error correction. A software defined radio (SDR) is a communication system which can implement different modulation schemes and tune to any frequency band by means of software that can control the programmable hardware. SDR utilizes the general purpose computer to perform certain signal processing techniques. We implement a turbo coding system using the Universal Software Radio Peripheral (USRP), a widely used SDR platform from Ettus. Detail configuration and performance comparison are also provided in this research.

This thesis deals with the decentralized autonomous system, in which individual nodes acting like peers, communicate and participate in collaborative tasks and decision making processes. An experimental test-bed is created using four Garcia robots. The robots act like peers and interact with each other using user datagram protocol (UDP) messages. Each robot continuously monitors for messages coming from other robots and respond accordingly. Each robot broadcasts its location to all the other robots within its vicinity. Robots do not have built-in global positioning system (GPS). So, an indoor localization method based on signal strength is developed to estimate robot's position. The signal strength that the robot gets from the nearby wireless access points is used to calculate the robot's position. Trilateration and fingerprint are some of the indoor localization methods used for this purpose. The communication functionality of the decentralized system has been tested and verified in the autonomous systems laboratory.

Sensor network plays a significant role in determining the performance of network inference tasks. A wireless sensor network with a large number of sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in WSN is developing an efficient protocol which has a significant impact on the convergence of the network. Parameter estimation is one of the most important applications of sensor network. In order to model such large and complex networks for estimation, efficient strategies and algorithms which take less time to converge are being developed. To deal with this challenge, an approach of having multilayer network structure to estimate parameter and reach convergence in less time is estimated by comparing it with known gossip distributed algorithm. Approached Multicast multilayer algorithm on a network structure of Gaussian mixture model with two components to estimate parameters were compared and simulated with gossip algorithm. Both the algorithms were compared based on the number of iterations the algorithms took to reach convergence by using Expectation Maximization Algorithm.Finally a series of theoretical and practical results that explicitly showed that Multicast works better than gossip in large and complex networks for estimation in consensus …

In the past few years, the study of electrical activity in the brain and its interactions with the body has become popular among researchers. One of the hottest topics related to brain activity is the epileptic seizure prediction. Currently, there are several techniques on how to predict a seizure; however, most of the techniques found in research papers are just mathematical models and not system implementations. The seizure prediction approach proposed in this thesis paper is achieved using the EMOTIV Epoc+ headset, MATLAB, and LabVIEW as the analog and digital signal processing devices. In addition, this thesis project incorporates the use of the Hilbert Huang transform (HHT) method to obtain intrinsic mode functions (IMF) and instantaneous frequency components of the transform. From the IMFs, features as variation coefficient (VC) and fluctuation indexes (FI) are extracted to feed a support vector machine that classifies the EEG data as pre-ictal and non-ictal EEGs. Outstanding patterns in non-ictal and pre-ictal are observed and demonstrated by significant differences between both types of EEG signals. In other words, a classification of EEG signals according to a category can be achieved proving that an epileptic seizure prediction technology has a future in engineering and biotechnology fields.

The focus of this thesis is on developing theories and techniques to extract lossy microwave filter parameters from data. In the literature, the Cauchy methods have been used to extract filters’ characteristic polynomials from measured scattering parameters. These methods are described and some examples are constructed to test their performance. The results suggest that the Cauchy method does not work well when the Q factors representing the loss of filters are not even. Based on some prototype filters and the relationship between Q factors and the loss, we conduct preliminary studies on alternative representations of the characteristic polynomials. The parameters in these new models are extracted using the Levenberg–Marquardt algorithm to accurately estimate characteristic polynomials and the loss information.

This thesis aims to increase photovoltaic (PV) panel power efficiency by employing a cooling system based on water circulation, which represents an improved version of water flow based active cooling systems. Theoretical calculations involved finding the heat produced by the PV panel and the circulation water flow required to remove this heat. A data logger and a cooling system for a test panel of 20W was designed and employed to study the relationship between the PV panel surface temperature and its output power. This logging and cooling system includes an Arduino microcontroller extended with a data logging shield, temperature sensing probes, current sensors, and a DC water pump. Real-time measurements were logged every minute for one or two day periods under various irradiance and air temperature conditions. For these experiments, a load resistance was chosen to operate the test panel at its maximum power point. Results indicate that the cooling system can yield an improvement of 10% in power production. Based on the observations from the test panel experiments, a cooling system was devised for a PV panel array of 640 W equipped with a commercial charge controller. The test data logger was repurposed for this larger system. An identical …

The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build …

Modern power generation aims to utilize renewable energy sources such as solar power and wind to supply customers with power. This approach avoids exhaustion of fossil fuels as well as provides clean energy. Microgrids have become popular over the years, as they contain multiple renewable power sources and battery storage systems to supply power to the entities within the network. These microgrids can share power with the main grid or operate islanded from the grid. During an islanded scenario, self-sustainability is crucial to ensure balance between supply and demand within the microgrid. This can be accomplished by a smart microgrid that can monitor system conditions and respond to power imbalance by shedding loads based on priority. Such a method ensures security of the most important loads in the system and manages energy by automatically disconnecting lower priority loads until system conditions have improved. This thesis introduces a prioritized load shedding algorithm for the microgrid at the University of North Texas Discovery Park and highlight how such an energy management algorithm can add reliability to an islanded microgrid.

In the first part of this thesis, we propose a distributed consensus algorithm under multi-layer multi-group structure with communication time delays. It is proven that the consensus will be achieved in both time-varying and fixed communication delays. In the second part, we study the distributed optimization problem with a finite-time mechanism. It is shown that our distributed proportional-integral algorithm can exponentially converge to the unique global minimizer when the gain parameters satisfy the sufficient conditions. Moreover, we equip the proposed algorithm with a decentralized algorithm, which enables an arbitrarily chosen agent to compute the exact global minimizer within a finite number of time steps, using its own states observed over a successive time steps. In the third part, it is shown the implementation of accelerated distributed energy management for microgrids is achieved. The results presented in the thesis are corroborated by simulations or experiments.

The Chief Security Officer Problem (CSO) consists of a CSO, a group of agents trying to communicate with the CSO and a group of eavesdroppers trying to listen to the conversations between the CSO and its agents. Through Lemmas and Theorems, several Information Theoretic questions are answered.

Mobility models serve as the foundation for evaluating and designing airborne networks. Due to the significant impact of mobility models on the network performance, mobility models for airborne networks (ANs) must realistically capture the attributes of ANs. In this paper, I develop a comprehensive modeling framework for ANs. The work I have done is concluded as the following three parts. First, I perform a comprehensive and comparative analysis of AN mobility models and evaluate the models based on several metrics: 1) networking performance, 2) ability to capture the mobility attributes of ANs, 3) randomness levels and 4) associated applications. Second, I develop two 3D mobility models and realistic boundary models. The mobility models follow physical laws behind aircraft maneuvering and therefore capture the characteristics of aircraft trajectories. Third, I suggest an estimation procedure to extract parameters in one of the models that I developed from real flight test data. The good match between the estimated trajectories and real flight trajectories also validate the suitability of the model. The mobility models and the estimation procedure lead to the creation of “realistic” simulation and evaluation environment for airborne networks.

Electrocardiography (ECG) signal transmitter is the device used to transmit the electrical signals of the heart to the remote machine. These electrical signals are ECG signals caused due to electrical activities in the heart. ECG signals have very low amplitude and frequency; hence amplification of the signals is needed to strengthen the signal. Conversion of the amplified signal into digital information and transmitting that information without losing any data is the key. This information is further used in monitoring the heart.

This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition and a feedback unit called head pose estimation is implemented to validate the correctness of predicted gestures. This entire system was developed using Python programming language and other supporting libraries like OpenCV, Tensor flow and Dlib to perform various image processing and machine learning tasks. This entire application can be deployed as a web application using Flask to make it operating system independent.

The ways that people use libraries have changed drastically over the past few decades. Proliferation of computers and the internet have led to the purpose of libraries expanding from being only places where information is stored, to spaces where people teach, learn, create, and collaborate. Due to this, the ways that people occupy the space in a library have also changed. To keep up with these changes and improve patron experience, institutions collect data to determine how their spaces are being used. This thesis involves the development a system that collects, stores, and analyzes data relevant to occupancy to learn how a space is being utilized. Data is collected from a temperature and humidity sensor, passive Infrared sensor, and an Infrared thermal sensor array to observe people as they occupy and move through a space. Algorithms were developed to analyze the collected sensor data to determine how many people are occupying a space or the directions that people are moving through a space. The algorithms demonstrate the ability to track multiple people moving through a space as well as count the number of people in a space with an RMSE of roughly 0.39 people.

Cognitive Radio (CR) technology creates the opportunity for unlicensed users to make use of the spectral band provided it does not interfere with any licensed user. It is a prominent tool with spectrum sensing functionality to identify idle channels and let the unlicensed users avail them. Thus, the CR technology provides the consumers access to a very large spectrum, quality spectral utilization, and energy efficiency due to spectral load balancing. However, the full potential of the CR technology can be realized only with CRs equipped with accurate mechanisms to predict/sense the spectral holes and vacant spectral bands without any prior knowledge about the characteristics of traffic in a real-time environment. Multi-layered perception (MLP), the popular neural network trained with the back-propagation (BP) learning algorithm, is a keen tool for classification of the spectral bands into "busy" or "idle" states without any a priori knowledge about the user system features. In this dissertation, we proposed the use of an evolutionary algorithm, Bacterial Foraging Optimization Algorithm (BFOA), for the training of the MLP NN. We have compared the performance of the proposed system with the traditional algorithm and with the Hybrid GA-PSO method. With the results of a simulation experiment that this …

Consumer drones have used both standards such as Wi-Fi as well as proprietary communication protocols, such as DJI's OcuSync. While these methods are well suited to certain flying scenarios, they are limited in range to around 4.3 miles. Government and military unmanned aerial vehicles (UAVs) controlled through satellites allow for a global reach in a low-latency environment. To address the range issue of commercial UAVs, this thesis investigates using standardized cellular technologies for command and control of UAV systems. The thesis is divided into five chapters: Chapter 1 is the introduction to the thesis. Chapter 2 describes the equipment used as well as the test setup. This includes the drone used, the cellular module used, the microcontroller used, and a description of the software written to collect the data. Chapter 3 describes the data collection goals, as well as locations in the sky that were flown in order to gather experimental data. Finally, the results are presented in Chapter 4, which draws limited correlation between the collected data and flight readiness Chapter 5 wraps up the thesis with a conclusion and future areas for research are also presented.

Coarse-grained fabrics are comprised of multi-bit configurable logic blocks and configurable interconnect. This work is focused on area and energy optimization techniques for coarse-grained reconfigurable fabric architectures. In this work, a variety of design techniques have been explored to improve the utilization of computational resources and increase energy savings. This includes splitting, folding, multi-level vertical interconnect. In addition to this, I have also studied fully connected homogeneous and heterogeneous architectures, and 3D architecture. I have also examined some of the hybrid strategies of computation unit’s arrangements. In order to perform energy and area analysis, I selected a set of signal and image processing benchmarks from MediaBench suite. I implemented various fabric architectures on 90nm ASIC process from Synopsys. Results show area improvement with energy savings as compared to baseline architecture.

An economically reliable technique for the synthesis of silicon nanowire was developed using silicon chloride as source material. The 30-40 micron long nanowires were found to have diameters ranging from 40 – 100 nm. An amorphous oxide shell covered the nanowires, post-growth. Raman spectroscopy confirmed the composition of the shell to be silicon-dioxide. Photoluminescence measurements of the as-grown nanowires showed green emission, attributed to the presence of the oxide shell. Etching of the oxide shell was found to decrease the intensity of green emission. n-type doping of the silicon nanowires was achieved using antimony as the dopant. The maximum dopant concentration was achieved by post-growth diffusion. Intrinsic nanowire parameters were determined by implementation of the as-grown and antimony doped silicon nanowires in field effect transistor configuration.

In general, Dual-Band technology enables microwave components to work at two different frequencies. This thesis introduces novel dual-band microwave components and their applications. Chapter 2 presents a novel compact dual-band balun (converting unbalanced signals to balanced ones). The ratio between two working frequencies is analyzed. A novel compact microstrip crossover (letting two lines to cross each other with very high isolation) and its dual-band application is the subject of chapter 3. A dual-frequency cloak based on lumped LC-circuits is introduced in chapter 4. In chapter 5, a dual-band RF device to detect dielectric constant changes of liquids in polydimethylsiloxane (PDMS) microfluidic channels has been presented. Such a device is very sensitive, and it has significantly improved the stability. Finally, conclusion of this thesis and future works are given in chapter 6.

This thesis presents the rate distortion analysis of conditional motion estimation, a process in which motion computation is restricted to only active pixels in the video. We model active pixels as independent and identically distributed Gaussian process and inactive pixels as Gaussian-Markov process and derive the rate distortion function based on conditional motion estimation. Rate-Distortion curves for the conditional motion estimation scheme are also presented. In addition this thesis also presents the hardware implementation of a block based motion estimation algorithm. Block matching algorithms are difficult to implement on FPGA chip due to its complexity. We implement 2D-Logarithmic search algorithm to estimate the motion vectors for the image. The matching criterion used in the algorithm is Sum of Absolute Differences (SAD). VHDL code for the motion estimation algorithm is verified using ISim and is implemented using Xilinx ISE Design tool. Synthesis results for the algorithm are also presented.